Optimization of Supercritical CO2 Brayton Cycle for Simple Cycle Gas Turbines Exhaust Heat Recovery Using Genetic Algorithm

Abstract

For the application of waste heat recovery (WHR), supercritical CO2 (S-CO2) Brayton power cycles offer significant suitable advantages such as compactness, low capital cost and applicable to a broad range of heat source temperatures. The current study is focused on thermodynamic modelling and optimization of Recuperated (RC) and Recuperated Recompression (RRC) S-CO2 Brayton cycles for exhaust heat recovery from a next generation heavy duty simple cycle gas turbine using a genetic algorithm. The Genetic Algorithm (GA) is mainly based on bio-inspired operators such as crossover, mutation and selection. This non-gradient based algorithm yields a simultaneous optimization of key S-CO2 Brayton cycle decision variables such as turbine inlet temperature, pinch point temperature difference, compressor pressure ratio. It also outputs optimized mass flow rate of CO2 for the fixed mass flow rate and temperature of the exhaust gas. The main goal of the optimization is to maximize power out of the exhaust stream which makes it single objective optimization. The optimization is based on thermodynamic analysis with suitable practical assumptions which can be varied according to the need of user. Further the optimal cycle design points are presented for both RC and RRC configurations and comparison of net power output is established for waste heat recovery.